Cross conference group stage vs divided conference group stage comparison

Steps:

Part 1: single run example

standard matchup weighting

Match result probabilities directly proportional to quality

  • weight of home/away win is home/away team’s quality score
  • weight of draw is mean of home/away team weight * a random value between 0 and 1
  • probabilites are each results weight divided by sum of weights
## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'

Part 2: 1000 simulation run

standard matchup weighting

Match result probabilities directly proportional to quality + weight of home/away win is home/away team’s quality score + weight of draw is mean of home/away team weight * a random value between 0 and 1 + probabilites are each results weight divided by sum of weights

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'

cubed matchup weighting

Match result probabilities directly proportional to quality

  • weight of home/away win is home/away team’s quality score cubed
  • weight of draw is mean of home/away team weight * a random value between 0 and 1
  • probabilites are each results weight divided by sum of weights
## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'

exponential matchup weighting

Match result probabilities directly proportional to quality

  • weight of home/away win is e raised to the home/away team’s quality score
  • weight of draw is mean of home/away team weight * a random value between 0 and 1
  • probabilites are each results weight divided by sum of weights
## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set
## `geom_smooth()` using formula 'y ~ x'